Association of radiomic features of skeletal muscle on CT images with muscle function and physical performance in older men.
Background Machine learning applied to computed tomography (CT) images captures variations in skeletal muscle texture and structure not detectable by conventional measures. These novel 'radiomic' features may offer added value in predicting muscle function and physical performance beyond traditional...
| Publicado en: | Age & Ageing Vol. 55; no. 3; pp. 1 - 12 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Mar2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192797126&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192797126 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00020729 AGA jtl: Age & Ageing issn: 00020729 maglogo: N pubinfo: dt: Mar2026 vid: 55 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 192797126 10.1093/ageing/afag057 ppf: 1 ppct: 11 formats: tig: atl: Association of radiomic features of skeletal muscle on CT images with muscle function and physical performance in older men. aug: au: Hetherington-Rauth, Megan Mansfield, Tyler A Lenchik, Leon Weaver, Ashley A Kado, Deborah M Lane, Nancy E Orwoll, Eric Cawthon, Peggy M affil: California Pacific Medical Center Research Institute, San Francisco, CA, USA Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, USA Department of Geriatric Medicine, Primary Care & Population Health, Stanford University School of Medicine, Palo Alto, CA, USAGeriatric Research Clinical and Education Center, Veterans Affairs Health System, Palo Alto, CA, USA Department of Medicine, University of California Davis, Davis, CA, USA Department of Endocrinology, Diabetes and Clinical Nutrition, Oregon Health & Science University, Portland, OR, USA California Pacific Medical Center Research Institute, San Francisco, CA, USADepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA su: Men's health Physical fitness Anthropometry Old age Skeletal muscle Research funding Computed tomography Radiomics Multiple regression analysis Questionnaires Body composition Frail elderly Descriptive statistics Diagnosis Gait in humans Longitudinal method Geriatric assessment Walking speed Body movement Comparative studies Data analysis software Factor analysis Grip strength Physical activity Sarcopenia sug: subj: Men's health Physical fitness Anthropometry Old age Irradiation Apparatus Manufacturing Fitness and Recreational Sports Centers Skeletal muscle Research funding Computed tomography Radiomics Multiple regression analysis Questionnaires Body composition Frail elderly Descriptive statistics Diagnosis Gait in humans Longitudinal method Geriatric assessment Walking speed Body movement Comparative studies Data analysis software Factor analysis Grip strength Physical activity Sarcopenia keyword: computed tomography copyrightHolder:British Geriatrics Society copyrightYear:2026 cross sectional area density fibrinogen grip strength https://dx.doi.org/10.1093/ageing/afag057 inLanguage:en leg machine learning muscle function older adult older people opportunistic publisher:Oxford University Press radiomics sameAs:https://pubmed.ncbi.nlm.nih.gov/41848761/ skeletal muscles thigh third lumbar vertebra trunk structure walking speed computed tomography copyrightHolder:British Geriatrics Society copyrightYear:2026 cross sectional area density fibrinogen grip strength https://dx.doi.org/10.1093/ageing/afag057 inLanguage:en leg machine learning muscle function older adult older people opportunistic publisher:Oxford University Press radiomics sameAs:https://pubmed.ncbi.nlm.nih.gov/41848761/ skeletal muscles thigh third lumbar vertebra trunk structure walking speed ab: Background Machine learning applied to computed tomography (CT) images captures variations in skeletal muscle texture and structure not detectable by conventional measures. These novel 'radiomic' features may offer added value in predicting muscle function and physical performance beyond traditional CT-derived muscle area and density. We aimed to identify radiomic features of skeletal muscle associated with grip strength, leg power and walking speed in older men. Methods In the Osteoporotic Fractures in Men study (n = 3404; 73.8 ± 5.9 years), participants underwent baseline CT scans (trunk L1, L3; right and left thigh) and assessments of grip strength, 6 m walk and leg power (Nottingham Power Rig). Muscle area and density were derived from automatically segmented CT images. Radiomic features were extracted using PyRadiomics. Elastic net regression and factor analysis identified key radiomic features; associations with muscle function/performance were assessed using regression models. Results Factor analysis identified nine factors for Trunk-L1 and eight for the other regions. Trunk-based factors significantly improved model fit for leg power, grip strength and walking speed (P < .05). Factor 1, representing body size and muscle texture complexity, was the most consistent predictor across outcomes. The Gray-Level Co-occurrence Matrix feature 'cluster prominence' was inversely associated with walking speed (β = −0.06 at L1; −0.05 at L3) and leg power (β = −0.05 at L1), independent of age, height, weight, muscle CSA, muscle density and technical group. Conclusion CT-derived radiomic features in the trunk region may reflect skeletal muscle structural characteristics that independently relate to strength, power and mobility in older men. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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